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	基于支持向量机的城市燃气日负荷预测方法研究
	张超 ; 刘奕 ; 张辉 ; 黄弘 ; ZHANG Chao ; LIU Yi ; ZHANG Hui ; HUANG Hong
	2016-03-30 ; 2016-03-30
关键词	燃气负荷预测 支持向量机 数据规则化方法 gas load forecasting support vector machine data normalization method TU996.3
其他题名	Study on urban short-term gas load forecasting based on support vector machine model
中文摘要	天然气是绿色、高效的能源,在工业生产和居民生活中应用日益广泛。天然气日负荷预测有助于科学合理地进行供应调配,预测结果对于实际工作具有重要参考价值。该文采用支持向量机方法对华北某城市的燃气实际日负荷数据进行了分析,建立了城市燃气日负荷预测模型。讨论了影响燃气日负荷变化的若干主要因素及其对燃气负荷预测建模的影响,分析了数据规则化方法对预测模型准确性的影响。该文建立的模型,对于全年负荷的预测误差小于5%;对于供暖期负荷的预测误差约为2%,结果较好。该文对建模影响因素和预测准确性的讨论,对类似问题有一定借鉴意义。; Natural gas is green and efficient energy which is widely used in industrial production and daily life.Daily gas load forecasting is helpful for scientifically and rationally supplying.Therefore,the forecasting results are beneficial to practical work.A forecasting model for daily gas loads was developed based on support vector machine theory.The gas load data of a North-China city were taken as a sample to verify the forecasting accuracy,with the main factors that affect the daily gas load as well as their effects on model accuracies being discussed.Several data normalization methods were used with the forecasting accuracy based on normalization methods analyzed.The developed model performs well with the error less than 5%for the through-year data,and less than 2%for the heating period data.The discussion about forecasting accuracies in this paper may be helpful for similar problems.
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://ir.lib.tsinghua.edu.cn/ir/item.do?handle=123456789/143142]
专题	清华大学
推荐引用方式 GB/T 7714	张超,刘奕,张辉,等. 基于支持向量机的城市燃气日负荷预测方法研究[J],2016, 2016.
APA	张超.,刘奕.,张辉.,黄弘.,ZHANG Chao.,...&HUANG Hong.(2016).基于支持向量机的城市燃气日负荷预测方法研究..
MLA	张超,et al."基于支持向量机的城市燃气日负荷预测方法研究".(2016).

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